<!DOCTYPE html>
<html>
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">
    <meta name="author" content="esy">
    
    <meta name="description" content="esy">
    
    
    
    
    
    
    <title>chatbot_sorry篇 | ESY</title>
    <link href="https://esyyes.github.io" rel="prefetch" />

    
<link rel="stylesheet" href="/css/bootstrap.min.css">
<link rel="stylesheet" href="/css/aos.css">
<link rel="stylesheet" href="/css/style.css">

    
<script src="/js/jquery.min.js"></script>

    
<script src="/js/bootstrap.min.js"></script>

    
<script src="/js/aos.js"></script>

    
<script src="/js/highslide/highslide-full.min.js"></script>

    
<link rel="stylesheet" href="/js/highslide/highslide.css">

    <style type="text/css">
        @media (max-width: 768px) {
            body {
                background-color: #f0f0f0;
                background: url('/imgs/xsbg.gif');
                background-attachment: fixed;
            }
        }
    </style>
    
    <!--<script type="text/javascript">
      if (document.images) {
        var avatar = new Image();
        avatar.src = '/imgs/avatar.jpg'
        var previews = 'preview1.jpg,preview2.jpg,preview3.jpg,preview4.jpg'.split(',')
        var previewsPreLoad = []
        for(var i = 0; i < length; i++) {
          previewsPreLoad.push(new Image())
          previewsPreLoad[previewsPreLoad.length - 1].src = '/imgs/preview' + previews[i]
        }
      }
    </script>-->
<meta name="generator" content="Hexo 5.2.0"></head>
<body>
    <!-- 背景轮播图功能 -->
    <section class="hidden-xs">
    <ul class="cb-slideshow">
        <li><span>天若</span></li>
        <li><span>有情</span></li>
        <li><span>天亦老</span></li>
        <li><span>我为</span></li>
        <li><span>长者</span></li>
        <li><span>续一秒</span></li>
    </ul>
</section>
    <!-- 欧尼酱功能, 谁用谁知道 -->
    
    <div class="gal-menu gal-dropdown">
    <div class="circle" id="gal">
        <div class="ring">
            <a href="https://esyyes.github.io" class="menuItem" style="left: 50%; top: 15%;">首页</a>
            
            <a class="menuItem" style="left: 80.3109%; top: 32.5%;">下一页</a>
            
            <a href="/archives" class="menuItem" style="left: 80.3109%; top: 67.5%;">归档</a>
            <a href="/about" class="menuItem" style="left: 50%; top: 85%;">关于</a>
            <a href="/message" class="menuItem" style="left: 19.6891%; top: 67.5%;">留言板</a>

            
            <a class="menuItem" style="left: 19.6891%; top: 32.5%;">上一页</a>
            
        </div>
        <audio id="audio" src="/imgs/oni.mp3"></audio>
    </div>
</div>
    
    <header class="navbar navbar-inverse" id="gal-header">
    <div class="container">
        <div class="navbar-header">
            <button type="button" class="navbar-toggle collapsed"
                    data-toggle="collapse" data-target=".bs-navbar-collapse"
                    aria-expanded="false">
                <span class="fa fa-lg fa-reorder"></span>
            </button>
            <a href="https://esyyes.github.io">
                
                <style>
                    #gal-header .navbar-brand {
                        height: 54px;
                        line-height: 24px;
                        font-size: 28px;
                        opacity: 1;
                        background-color: rgba(0,0,0,0);
                        text-shadow: 0 0 5px #fff,0 0 10px #fff,0 0 15px #fff,0 0 20px #228DFF,0 0 35px #228DFF,0 0 40px #228DFF,0 0 50px #228DFF,0 0 75px #228DFF;
                    }
                </style>
                <!-- 这里使用文字(navbar_text or config.title) -->
                <div class="navbar-brand">ESY</div>
                
            </a>
        </div>
        <div class="collapse navbar-collapse bs-navbar-collapse">
            <ul class="nav navbar-nav" id="menu-gal">
                
                
                <li class="">
                    <a href="/">
                        <i class="fa fa-home"></i>首页
                    </a>
                </li>
                
                
                
                <li class="">
                    <a href="/archives">
                        <i class="fa fa-archive"></i>归档
                    </a>
                </li>
                
                
                
                
                <li class="dropdown">
                    <!-- TODO 添加hover dropdown效果 -->
                    <a href="#" class="dropdown-toggle" data-toggle="dropdown"
                       aria-haspopup="true" aria-expanded="false" data-hover="dropdown">
                        <i class="fa fa-list"></i>分类
                    </a>
                    <ul class="dropdown-menu">
                        
                        
                        <li>
                            <a href="/categories/py-study/">py_study</a>
                        </li>
                        
                        <li>
                            <a href="/categories/nlp/">nlp</a>
                        </li>
                        
                        <li>
                            <a href="/categories/Graduation-work/">Graduation work</a>
                        </li>
                        
                        <li>
                            <a href="/categories/work/">work</a>
                        </li>
                        
                        <li>
                            <a href="/categories/hexo/">hexo</a>
                        </li>
                        
                        <li>
                            <a href="/categories/hexo%E5%AE%8C%E5%96%84/">-hexo完善</a>
                        </li>
                        
                        
                        <li>
                            <a href="/categories">...</a>
                        </li>
                        
                        
                    </ul>
                </li>
                
                
                
                
                
                <li class="dropdown">
                    <!-- TODO 添加hover dropdown效果 -->
                    <a href="#" class="dropdown-toggle" data-toggle="dropdown"
                       aria-haspopup="true" aria-expanded="false" data-hover="dropdown">
                        <i class="fa fa-tags"></i>标签
                    </a>
                    <ul class="dropdown-menu">
                        
                        
                        <li>
                            <a href="/tags/py-study/">py_study</a>
                        </li>
                        
                        <li>
                            <a href="/tags/nlp/">nlp</a>
                        </li>
                        
                        <li>
                            <a href="/tags/Graduation-work/">Graduation work</a>
                        </li>
                        
                        <li>
                            <a href="/tags/work/">work</a>
                        </li>
                        
                        <li>
                            <a href="/tags/hexo/">hexo</a>
                        </li>
                        
                        <li>
                            <a href="/tags/%E4%B8%AA%E4%BA%BA%E5%8D%9A%E5%AE%A2%E6%90%AD%E5%BB%BA/">-个人博客搭建</a>
                        </li>
                        
                        
                        <li>
                            <a href="/tags">...</a>
                        </li>
                        
                        
                    </ul>
                </li>
                
                
                
                
                <li class="">
                    <a href="/about">
                        <i class="fa fa-user"></i>关于我
                    </a>
                </li>
                
                
            </ul>
        </div>
    </div>
</header>
    <div id="gal-body">
        <div class="container">
            <div class="row">
                <div class="col-md-8 gal-right" id="mainstay">
                    
<article class="article well article-body" id="article">
    <div class="breadcrumb">
        <i class="fa fa-home"></i>
        <a href="https://esyyes.github.io">ESY</a>
        >
        <span>chatbot_sorry篇</span>
    </div>
    <!-- 大型设备详细文章 -->
    <div class="hidden-xs">
        <div class="title-article">
            <h1>
                <a href="/2020/07/22/python%20work/chatbot-sorry%E7%AF%87/">chatbot_sorry篇</a>
            </h1>
        </div>
        <div class="tag-article">
            
            <span class="label label-gal">
                <i class="fa fa-tags"></i>
                
                <a href="/tags/work/">work</a>
                
            </span>
            
            <span class="label label-gal">
                <i class="fa fa-calendar"></i> 2020-07-22
            </span>
            
        </div>
    </div>
    <!-- 小型设备详细文章 -->
    <div class="visible-xs">
        <center>
            <div class="title-article">
                <h4>
                    <a href="/2020/07/22/python%20work/chatbot-sorry%E7%AF%87/">chatbot_sorry篇</a>
                </h4>
            </div>
            <p>
                <i class="fa fa-calendar"></i> 2020-07-22
            </p>
            <p>
                
                <i class="fa fa-tags"></i>
                
                <a href="/tags/work/">work</a>
                
                
                
            </p>
        </center>
    </div>
    <div class="content-article">
        <h1 id="chatbot-sorry篇"><a href="#chatbot-sorry篇" class="headerlink" title="chatbot_sorry篇"></a>chatbot_sorry篇</h1><p>直接循环输入语句。判断哪些句子无法被检测</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br></pre></td><td class="code"><pre><span class="line"># -*- coding: utf-8 -*-</span><br><span class="line"># @Time     : 2020&#x2F;7&#x2F;22</span><br><span class="line"># @Author   : esy</span><br><span class="line"></span><br><span class="line">from chatbot_21 import *</span><br><span class="line">import pandas as pd</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">data &#x3D; pd.read_csv(&#39;Q&amp;A pairs.csv&#39;)</span><br><span class="line">print(&quot;Hello, I&#39;m a question-and-answer chatbot for the tourism domain based on retrieval mode.&quot;</span><br><span class="line">      &quot;If you want to exit, input &#39;Bye&#39;!&quot;)</span><br><span class="line">greeting_output &#x3D; [&quot;hi&quot;, &quot;hey&quot;, &quot;hello&quot;, &quot;I&#39;m glad! You are talking to me&quot;]</span><br><span class="line"></span><br><span class="line">for i in range(len(data)):</span><br><span class="line">    question &#x3D; np.array(data[&#39;Question&#39;]).tolist()</span><br><span class="line">    text2 &#x3D; question[i]</span><br><span class="line">    # 将词还原到最基础模式</span><br><span class="line">    text1 &#x3D; &quot; &quot;.join([token.lemma_ for token in nlp(text2)])</span><br><span class="line">    # 进行简单回复</span><br><span class="line">    if text1 &#x3D;&#x3D; &#39;hey&#39; or text1 &#x3D;&#x3D; &#39;hi&#39; or text1 &#x3D;&#x3D; &#39;hello&#39; or text1 &#x3D;&#x3D; &#39;HI&#39;:</span><br><span class="line">        print(random.choice(greeting_output))</span><br><span class="line">    elif text1 &#x3D;&#x3D; &#39;thank&#39; or text1 &#x3D;&#x3D; &#39;thank -PRON-&#39; or text1 &#x3D;&#x3D; &#39;THANK&#39;:</span><br><span class="line">        print(&#39;You are welcome.&#39;)</span><br><span class="line">    elif text1 &#x3D;&#x3D; &#39;bye&#39; or text1 &#x3D;&#x3D; &#39;BYE&#39;:</span><br><span class="line">        print(&#39;Bye!&#39;)</span><br><span class="line">    elif len(text1.split()) &lt; 3:</span><br><span class="line">        print(f&quot;I&#39;m sorry. I don&#39;t understand you&quot;)</span><br><span class="line">        print(f&#39;第&#123;i+1&#125;个由于n&lt;3&#39;)</span><br><span class="line">    else:</span><br><span class="line">        if QA_tf_idf(data, del_stop(text1)) &#x3D;&#x3D; 0:</span><br><span class="line">            print(f&quot;I&#39;m sorry. I don&#39;t understand you&quot;)</span><br><span class="line">            print(f&#39;第&#123;i+1&#125;个由于return&#x3D;0&#39;)</span><br><span class="line">        else:</span><br><span class="line">            tf_idf1, quet_tfidf1, scores1 &#x3D; QA_tf_idf(data, del_stop(text1))</span><br><span class="line">            recall_ques1, recall_answ1, recall_tf_idf_1 &#x3D; recall_5(scores1, data, tf_idf1)</span><br><span class="line">            # print(f&#39;召回的5个问题&#39;)</span><br><span class="line">            # for j in range(5):</span><br><span class="line">                # print(f&#39;第&#123;j + 1&#125;问题：&#123;recall_ques1[j]&#125;&#39;, end&#x3D;&#39;\t&#39;)</span><br><span class="line">                # print(f&#39;相似度：&#123;similar_list(quet_tfidf1, recall_tf_idf_1)[j]&#125;&#39;)</span><br><span class="line">            # print(f&#39;最佳答案：&#123;best_answer(similar_list(quet_tfidf1, recall_tf_idf_1), recall_answ1)&#125;&#39;)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br></pre></td><td class="code"><pre><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">4</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">5</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">9</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">15</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">18</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">29</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">30</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">85</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">88</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">95</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">98</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br><span class="line">I<span class="string">&#x27;m sorry. I don&#x27;</span>t understand you</span><br><span class="line">第<span class="number">99</span>个由于<span class="keyword">return</span>=<span class="number">0</span></span><br></pre></td></tr></table></figure>

<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br><span class="line">106</span><br><span class="line">107</span><br><span class="line">108</span><br><span class="line">109</span><br><span class="line">110</span><br><span class="line">111</span><br><span class="line">112</span><br><span class="line">113</span><br><span class="line">114</span><br><span class="line">115</span><br><span class="line">116</span><br><span class="line">117</span><br><span class="line">118</span><br><span class="line">119</span><br><span class="line">120</span><br><span class="line">121</span><br><span class="line">122</span><br><span class="line">123</span><br><span class="line">124</span><br><span class="line">125</span><br><span class="line">126</span><br><span class="line">127</span><br><span class="line">128</span><br><span class="line">129</span><br><span class="line">130</span><br><span class="line">131</span><br><span class="line">132</span><br><span class="line">133</span><br><span class="line">134</span><br><span class="line">135</span><br><span class="line">136</span><br><span class="line">137</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/7/22</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> math</span><br><span class="line"><span class="keyword">import</span> en_core_web_md</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"><span class="keyword">from</span> collections <span class="keyword">import</span> defaultdict</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line">nlp = en_core_web_md.load()</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 预处理文本数据，将单词还原成基础模式，小写，删除停靠词，得到关键词</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">del_stop</span>(<span class="params">text</span>):</span></span><br><span class="line">    token_doc = [token.lemma_ <span class="keyword">for</span> token <span class="keyword">in</span> nlp(text)]</span><br><span class="line">    <span class="comment"># 去除停用词后创建单词列表</span></span><br><span class="line">    filtered_sentence = []</span><br><span class="line">    <span class="keyword">for</span> word <span class="keyword">in</span> token_doc:</span><br><span class="line">        lexeme = nlp.vocab[word]</span><br><span class="line">        <span class="keyword">if</span> <span class="keyword">not</span> lexeme.is_stop != <span class="literal">False</span>:</span><br><span class="line">            filtered_sentence.append(word)</span><br><span class="line">    <span class="keyword">return</span> filtered_sentence</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 计算tfidf得分</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">QA_tf_idf</span>(<span class="params">data, filtered_sentence</span>):</span></span><br><span class="line">    question = np.array(data[<span class="string">&#x27;Question&#x27;</span>]).tolist()</span><br><span class="line">    <span class="comment"># 将问题进行分词</span></span><br><span class="line">    list_ques = [[t.lemma_ <span class="keyword">for</span> t <span class="keyword">in</span> nlp(question[i])] <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line">    <span class="comment"># 去除停用词后创建单词列表</span></span><br><span class="line">    list_key = []</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data)):</span><br><span class="line">        filtered_sentence1 = []</span><br><span class="line">        <span class="keyword">for</span> word <span class="keyword">in</span> list_ques[i]:</span><br><span class="line">            lexeme = nlp.vocab[word]</span><br><span class="line">            <span class="keyword">if</span> <span class="keyword">not</span> lexeme.is_stop != <span class="literal">False</span>:</span><br><span class="line">                filtered_sentence1.append(word)</span><br><span class="line">        list_key.append(filtered_sentence1)</span><br><span class="line">    <span class="comment"># 统计词频和词汇,看单词出现的次数</span></span><br><span class="line">    doc_frequency = defaultdict(<span class="built_in">int</span>)</span><br><span class="line">    list_words = list_key</span><br><span class="line">    <span class="keyword">for</span> word_list <span class="keyword">in</span> list_words:</span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> word_list:</span><br><span class="line">            doc_frequency[i] += <span class="number">1</span></span><br><span class="line">    l1 = <span class="built_in">set</span>(filtered_sentence)</span><br><span class="line">    l2 = <span class="built_in">set</span>(doc_frequency.keys())</span><br><span class="line"></span><br><span class="line">    <span class="keyword">if</span> <span class="keyword">not</span> l1.issubset(l2) != <span class="literal">False</span>:</span><br><span class="line">        <span class="keyword">return</span> <span class="number">0</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="comment"># 计算每个词的IDF值</span></span><br><span class="line">        word_idf = &#123;&#125;  <span class="comment"># 存储每个词的idf值</span></span><br><span class="line">        word_doc = defaultdict(<span class="built_in">int</span>)  <span class="comment"># 存储包含该词的文档数</span></span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> doc_frequency:</span><br><span class="line">            <span class="keyword">for</span> j <span class="keyword">in</span> list_words:</span><br><span class="line">                <span class="keyword">if</span> i <span class="keyword">in</span> j:</span><br><span class="line">                    word_doc[i] += <span class="number">1</span></span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> doc_frequency:</span><br><span class="line">            word_idf[i] = math.log(<span class="built_in">len</span>(list_key) / (word_doc[i] + <span class="number">1</span>))</span><br><span class="line"></span><br><span class="line">        <span class="comment"># 对样本进行词频统计</span></span><br><span class="line">        list_doc = []</span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(list_key)):</span><br><span class="line">            doc_frequency1 = defaultdict(<span class="built_in">int</span>)</span><br><span class="line">            <span class="keyword">for</span> j <span class="keyword">in</span> list_key[i]:</span><br><span class="line">                doc_frequency1[j] += <span class="number">1</span></span><br><span class="line">            list_doc.append(doc_frequency1)</span><br><span class="line">        <span class="comment"># 计算语料库中每个词的tf_idf,构建向量</span></span><br><span class="line">        tf_idf = []</span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data)):</span><br><span class="line">            tf_idf.append([word_idf[i] * list_doc[j][i] / <span class="built_in">len</span>(list_key[j]) <span class="keyword">for</span> i <span class="keyword">in</span> (list_doc[j])])</span><br><span class="line"></span><br><span class="line">        doc_frequency2 = defaultdict(<span class="built_in">int</span>)</span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> filtered_sentence:</span><br><span class="line">            doc_frequency2[i] += <span class="number">1</span></span><br><span class="line">        <span class="comment"># 计算问题对应语料库的tf-idf得分</span></span><br><span class="line">        scores = []</span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data)):</span><br><span class="line">            score = <span class="number">0</span></span><br><span class="line">            <span class="keyword">for</span> i <span class="keyword">in</span> doc_frequency2:</span><br><span class="line">                score += (word_idf[i] * list_doc[j][i] / <span class="built_in">len</span>(list_key[j]))</span><br><span class="line">            scores.append(score)</span><br><span class="line">        <span class="comment"># 直接计算问题的语料库得分</span></span><br><span class="line">        <span class="comment"># 对问题进行词频统计</span></span><br><span class="line"></span><br><span class="line">        quet_tfidf = []</span><br><span class="line">        <span class="keyword">for</span> i <span class="keyword">in</span> doc_frequency2:</span><br><span class="line">            quet_tfidf.append(word_idf[i] * doc_frequency2[i] / <span class="built_in">len</span>(filtered_sentence))</span><br><span class="line">    <span class="keyword">return</span> tf_idf, quet_tfidf, scores</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">recall_5</span>(<span class="params">scores, data, tf_idf</span>):</span></span><br><span class="line">    question = np.array(data[<span class="string">&#x27;Question&#x27;</span>]).tolist()</span><br><span class="line">    answer = np.array(data[<span class="string">&#x27;Answer&#x27;</span>]).tolist()</span><br><span class="line">    <span class="comment"># 用字典形式按照升序形式排序</span></span><br><span class="line">    x = np.arange(<span class="built_in">len</span>(data)).tolist()</span><br><span class="line">    dict_score = <span class="built_in">dict</span>(<span class="built_in">zip</span>(x, scores))</span><br><span class="line">    listc = <span class="built_in">sorted</span>(<span class="built_in">zip</span>(dict_score.values(), dict_score.keys()))</span><br><span class="line">    recall_ques = []</span><br><span class="line">    recall_answ = []</span><br><span class="line">    recall_tf_idf = []</span><br><span class="line">    <span class="comment"># 召回得分最高的5个问题</span></span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">6</span>):</span><br><span class="line">        recall_ques.append(question[listc[-i][<span class="number">1</span>]])</span><br><span class="line">        recall_answ.append(answer[listc[-i][<span class="number">1</span>]])</span><br><span class="line">        recall_tf_idf.append(tf_idf[listc[-i][<span class="number">1</span>]])</span><br><span class="line">    <span class="keyword">return</span> recall_ques, recall_answ, recall_tf_idf</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 求5个问题的相似度</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">similar_list</span>(<span class="params">quet_tfidf, recall_tf_idf</span>):</span></span><br><span class="line">    similar_score = []</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(recall_tf_idf)):</span><br><span class="line">        <span class="keyword">if</span> <span class="built_in">len</span>(recall_tf_idf[i]) == <span class="built_in">len</span>(quet_tfidf):</span><br><span class="line">            similar_score.append(np.dot(quet_tfidf, recall_tf_idf[i]) / (np.linalg.norm(quet_tfidf) * np.linalg.norm(recall_tf_idf[i])))</span><br><span class="line">        <span class="keyword">elif</span> <span class="built_in">len</span>(recall_tf_idf[i]) &lt; <span class="built_in">len</span>(quet_tfidf):</span><br><span class="line">            <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(quet_tfidf) - <span class="built_in">len</span>(recall_tf_idf[i])):</span><br><span class="line">                c = recall_tf_idf[i]</span><br><span class="line">                c.append(<span class="number">0</span>)</span><br><span class="line">            similar_score.append(np.dot(quet_tfidf, c) / (np.linalg.norm(quet_tfidf) * np.linalg.norm(recall_tf_idf[i])))</span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">abs</span>(<span class="built_in">len</span>(quet_tfidf) - <span class="built_in">len</span>(recall_tf_idf[i]))):</span><br><span class="line">                a = quet_tfidf</span><br><span class="line">                a.append(<span class="number">0</span>)</span><br><span class="line">            similar_score.append(np.dot(a, recall_tf_idf[i]) / (np.linalg.norm(a) * np.linalg.norm(recall_tf_idf[i])))</span><br><span class="line">    <span class="keyword">return</span> similar_score</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 输出相似度最高的那个的答案</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">best_answer</span>(<span class="params">list_num, recall_answ</span>):</span></span><br><span class="line">    <span class="keyword">if</span> <span class="built_in">max</span>(list_num) &lt;= <span class="number">0.2</span>:</span><br><span class="line">        <span class="keyword">return</span> <span class="string">f&quot;I&#x27;m sorry. I don&#x27;t understand you&quot;</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        best_ans = recall_answ[list_num.index(<span class="built_in">max</span>(list_num))]</span><br><span class="line">        <span class="keyword">return</span> best_ans</span><br></pre></td></tr></table></figure>

<h2 id="问题查询："><a href="#问题查询：" class="headerlink" title="问题查询："></a>问题查询：</h2><h3 id="1-查看去除停靠词后句子"><a href="#1-查看去除停靠词后句子" class="headerlink" title="1.查看去除停靠词后句子"></a>1.查看去除停靠词后句子</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">sl = <span class="built_in">set</span>([<span class="number">1</span>, <span class="number">2</span>,<span class="number">3</span>])</span><br><span class="line">sl2 = <span class="built_in">set</span>([<span class="number">2</span>,<span class="number">3</span>, <span class="number">7</span>,<span class="number">9</span>])</span><br><span class="line">sl.issubset(sl2)</span><br><span class="line">Out[<span class="number">5</span>]: <span class="literal">False</span></span><br></pre></td></tr></table></figure>

<p>感觉是去除词频后，没有包含进去</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># if not l1.issubset(l2) != False:</span></span><br><span class="line"><span class="comment">#     if not True != &#123;&#x27;-pron-&#x27;&#125;.issubset(l1):</span></span><br><span class="line"><span class="comment">#         doc_frequency2[&#x27;-PRON-&#x27;] = doc_frequency2[&#x27;-pron-&#x27;]</span></span><br></pre></td></tr></table></figure>

<p>经查验，经过问题的分词后，人输出为pron，而语料库中是大写的PRON</p>
<p>sl.issubset(sl2)是属于包含关系，需要调整为</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">if</span> <span class="built_in">len</span>(l1 &amp; l2) == <span class="number">0</span>:</span><br><span class="line">    <span class="keyword">return</span> <span class="number">0</span></span><br></pre></td></tr></table></figure>

<p>判断两个集合是否有交集，这样即使输入的内容有些关键词汇不在语料库中也能直接输出。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">run_chatbot</span>(<span class="params">text2, data</span>):</span></span><br><span class="line">    text1 = <span class="string">&quot; &quot;</span>.join([token.lemma_ <span class="keyword">for</span> token <span class="keyword">in</span> nlp(text2)])</span><br><span class="line">    <span class="keyword">if</span> text1 == <span class="string">&#x27;hey&#x27;</span> <span class="keyword">or</span> text1 == <span class="string">&#x27;hi&#x27;</span> <span class="keyword">or</span> text1 == <span class="string">&#x27;hello&#x27;</span> <span class="keyword">or</span> text1 == <span class="string">&#x27;HI&#x27;</span>:</span><br><span class="line">        <span class="keyword">return</span> random.choice(greeting_output)</span><br><span class="line">    <span class="keyword">elif</span> text1 == <span class="string">&#x27;thank&#x27;</span> <span class="keyword">or</span> text1 == <span class="string">&#x27;thank -PRON-&#x27;</span> <span class="keyword">or</span> text1 == <span class="string">&#x27;THANK&#x27;</span>:</span><br><span class="line">        <span class="keyword">return</span> <span class="string">&#x27;You are welcome.&#x27;</span></span><br><span class="line">    <span class="keyword">elif</span> text1 == <span class="string">&#x27;bye&#x27;</span> <span class="keyword">or</span> text1 == <span class="string">&#x27;BYE&#x27;</span>:</span><br><span class="line">        <span class="keyword">return</span> <span class="string">&#x27;Bye!&#x27;</span></span><br><span class="line">    <span class="keyword">elif</span> <span class="built_in">len</span>(text1.split()) &lt; <span class="number">3</span>:</span><br><span class="line">        <span class="keyword">return</span> <span class="string">&quot;I&#x27;m sorry. I don&#x27;t understand you&quot;</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">if</span> QA_tf_idf(data, del_stop(text2)) == <span class="number">0</span>:</span><br><span class="line">            <span class="keyword">return</span> <span class="string">&quot;I&#x27;m sorry. I don&#x27;t understand you&quot;</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            tf_idf1, quet_tfidf1, scores1 = QA_tf_idf(data, del_stop(text2))</span><br><span class="line">            recall_ques1, recall_answ1, recall_tf_idf_1 = recall_5(scores1, data, tf_idf1)</span><br><span class="line">            <span class="keyword">return</span> best_answer(similar_list(quet_tfidf1, recall_tf_idf_1), recall_answ1)</span><br></pre></td></tr></table></figure>

<p>也同时对输出的语句进行规制的判定，如果不在规制内，输入的单词小于3个也直接输出</p>
<h3 id="2-相似度问题"><a href="#2-相似度问题" class="headerlink" title="2.相似度问题"></a>2.相似度问题</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">similar_spacy</span>(<span class="params">text, recall_ques</span>):</span></span><br><span class="line">    similar_goal = []</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(recall_ques)):</span><br><span class="line">        similar_goal.append(nlp(text).similarity(nlp(recall_ques[i])))</span><br><span class="line">    <span class="keyword">return</span> similar_goal</span><br></pre></td></tr></table></figure>

<p>直接利用自带的相似度，去估计两个输入的大小，这样会降低长度不等的问题的影响</p>
<p><img src="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1595481286576&di=df81997c696a3f9ea906c1ee50ed1cc4&imgtype=0&src=http://c-ssl.duitang.com/uploads/item/201603/18/20160318202414_yBjSX.jpeg"></p>

    </div>
</article>


                </div>
                <aside class="col-md-4 gal-left" id="sidebar">
    <!-- 此为sidebar的搜索框, 非搜索结果页面 -->
<aside id="sidebar-search">
    <div class="search hidden-xs" data-aos="fade-up" data-aos-duration="2000">
        <form class="form-inline clearfix" id="search-form" method="get"
              action="/search/index.html">
            <input type="text" name="s" class="form-control" id="searchInput" placeholder="搜索文章~" autocomplete="off">
            <button class="btn btn-danger btn-gal" type="submit">
                <i class="fa fa-search"></i>
            </button>
        </form>
    </div>
</aside>
    <aside id="sidebar-author">
    <div class="panel panel-gal" data-aos="flip-right" data-aos-duration="3000">
        <div class="panel-heading" style="text-align: center">
            <i class="fa fa-quote-left"></i>
            esy
            <i class="fa fa-quote-right"></i>
        </div>
        <div class="author-panel text-center">
            <img src="/imgs/avatar.jpg" width="140" height="140"
                 alt="个人头像" class="author-image">
            <p class="author-description"><p>esy</p>
</p>
        </div>
    </div>
</aside>
    
    <aside id="sidebar-recent_comments">
    <div class="panel panel-gal recent hidden-xs" data-aos="fade-up" data-aos-duration="2000">
        <div class="panel-heading">
            <i class="fa fa-comments"></i>
            最新评论
            <i class="fa fa-times-circle panel-remove"></i>
            <i class="fa fa-chevron-circle-up panel-toggle"></i>
        </div>
        <ul class="list-group list-group-flush"></ul>
    </div>
</aside>
    
    <!-- 要配置好leancloud才能开启此小工具 -->
    
    
    <aside id="sidebar-recent_posts">
    <div class="panel panel-gal recent hidden-xs" data-aos="fade-up" data-aos-duration="2000">
        <div class="panel-heading">
            <i class="fa fa-refresh"></i>
            近期文章
            <i class="fa fa-times-circle panel-remove"></i>
            <i class="fa fa-chevron-circle-up panel-toggle"></i>
        </div>
        <ul class="list-group list-group-flush">
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/11/05/python%20work/%E6%9C%80%E5%B0%8F%E4%BA%8C%E4%B9%98%E6%B3%95/">最小二乘法</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/11/05/python%20work/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0-%E7%AC%AC%E4%B8%80%E7%AB%A0/">统计学习--第一章</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/11/04/python%20work/hello-world/">Hello World</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/11/03/python%20work/%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AF%84%E4%BC%B0%E6%8C%87%E6%A0%87/">分类模型的评估指标</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/10/21/python%20work/10-21-%E7%88%AC%E8%99%AB%E5%9F%BA%E7%A1%80/">10-21 爬虫基础</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/09/25/python%20work/CRF%E7%9A%84%E6%95%B4%E4%BD%93%E6%B5%81%E7%A8%8B%E7%BB%93%E6%9E%9C/">CRF的整体流程结果</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/09/25/python%20work/nlp-crf%E6%A8%A1%E5%9E%8B/">nlp_crf模型</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/09/25/python%20work/%E6%95%B0%E5%AD%A6%E5%BB%BA%E6%A8%A1%E9%97%AE%E9%A2%983/">数学建模问题3</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/09/25/python%20work/%E6%95%B0%E5%AD%A6%E5%BB%BA%E6%A8%A1%E9%97%AE%E9%A2%982/">数学建模问题2</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/09/25/python%20work/%E6%95%B0%E5%AD%A6%E5%BB%BA%E6%A8%A1%E9%97%AE%E9%A2%981/">数学建模问题1</a>
                </span>
            </li>
            
        </ul>
    </div>
</aside>
    
    
    <aside id="sidebar-rand_posts">
    <div class="panel panel-gal recent hidden-xs" data-aos="fade-up" data-aos-duration="2000">
        <div class="panel-heading">
            <i class="fa fa-refresh"></i>
            随机文章
            <i class="fa fa-times-circle panel-remove"></i>
            <i class="fa fa-chevron-circle-up panel-toggle"></i>
        </div>
        <ul class="list-group list-group-flush">
            
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/09/03/python%20work/DATA%E6%95%B0%E6%8D%AE%E7%BB%98%E5%88%B6/">DATA数据绘制</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2019/12/03/python%20work/hexo%E5%AD%A6%E4%B9%A0/">hexo学习</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2019/11/24/python%20work/pyhon%E5%9F%BA%E7%A1%80-%E5%87%BD%E6%95%B0/">pyhon基础--函数</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/05/26/python%20work/python-%E5%85%A5%E9%97%A8%E5%A4%8D%E4%B9%A0%E4%B9%8B%E6%A8%A1%E5%9D%97%E5%92%8C%E5%8C%85/">python-入门复习之模块和包</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2019/12/26/python%20work/python%E8%B0%83%E7%94%A8wfdb/">LV-3</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/06/29/python%20work/%E4%B8%AD%E6%9C%9F%E7%AD%94%E8%BE%A9%E9%A2%98%E7%9B%AE/">中期答辩题目</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/11/03/python%20work/%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AF%84%E4%BC%B0%E6%8C%87%E6%A0%87/">分类模型的评估指标</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2019/12/26/python%20work/%E5%8E%BB%E8%B6%8B%E5%8A%BF%E6%B3%A2%E5%8A%A8%E5%88%86%E6%9E%90DFA/">去趋势波动分析DFA</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/07/10/python%20work/%E8%81%8A%E5%A4%A9%E6%9C%BA%E5%99%A8%E4%BA%BA%E5%9F%BA%E7%A1%80/">聊天机器人基础</a>
                </span>
            </li>
            
            <li class="list-group-item">
                <span class="post-title">
                    <a href="/2020/07/27/python%20work/%E6%A0%B7%E6%9C%AC%E6%95%B0%E6%8D%AE%E7%9A%84%E7%94%9F%E6%88%90/">样本数据的生成</a>
                </span>
            </li>
            
        </ul>
    </div>
</aside>
    
    
    <aside id="gal-sets">
        <div class="panel panel-gal hidden-xs" data-aos="fade-up" data-aos-duration="2000">
            <ul class="nav nav-pills pills-gal">

                
                <li>
                    <a href="/2020/07/22/python%20work/chatbot-sorry%E7%AF%87/index.html#sidebar-tags" data-toggle="tab" id="tags-tab">热门标签</a>
                </li>
                
                
                <li>
                    <a href="/2020/07/22/python%20work/chatbot-sorry%E7%AF%87/index.html#sidebar-friend-links" data-toggle="tab" id="friend-links-tab">友情链接</a>
                </li>
                
                
                <li>
                    <a href="/2020/07/22/python%20work/chatbot-sorry%E7%AF%87/index.html#sidebar-links" data-toggle="tab" id="links-tab">个人链接</a>
                </li>
                
            </ul>
            <div class="tab-content">
                
                <div class="cloud-tags tab-pane nav bs-sidenav fade" id="sidebar-tags">
    
    <a href="/tags/py-study/" style="font-size: 17.975163073510174px;" class="tag-cloud-link">py_study</a>
    
    <a href="/tags/nlp/" style="font-size: 15.52054383867063px;" class="tag-cloud-link">nlp</a>
    
    <a href="/tags/Graduation-work/" style="font-size: 10.662574241531882px;" class="tag-cloud-link">Graduation work</a>
    
    <a href="/tags/work/" style="font-size: 18.494575899308934px;" class="tag-cloud-link">work</a>
    
    <a href="/tags/hexo/" style="font-size: 17.170219625663545px;" class="tag-cloud-link">hexo</a>
    
    <a href="/tags/%E4%B8%AA%E4%BA%BA%E5%8D%9A%E5%AE%A2%E6%90%AD%E5%BB%BA/" style="font-size: 12.024911815038784px;" class="tag-cloud-link">-个人博客搭建</a>
    
    <a href="/tags/malab-%E6%AF%95%E4%B8%9A/" style="font-size: 16.66682984641338px;" class="tag-cloud-link">-malab -毕业</a>
    
    <a href="/tags/python-%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/" style="font-size: 14.873253581643993px;" class="tag-cloud-link">-python -人工智能</a>
    
    <a href="/tags/python/" style="font-size: 18.5536896626095px;" class="tag-cloud-link">python</a>
    
    <a href="/tags/python/" style="font-size: 13.170184778694363px;" class="tag-cloud-link">-python</a>
    
    <a href="/tags/mathematical-modeling/" style="font-size: 10.168138450918086px;" class="tag-cloud-link">mathematical modeling</a>
    
    <a href="/tags/statistical-learning/" style="font-size: 12.366084261748991px;" class="tag-cloud-link">statistical learning</a>
    
</div>
                
                
                <div class="friend-links tab-pane nav bs-sidenav fade" id="sidebar-friend-links">
    
    <li>
        <a href="http://kdays.net/days/" target="_blank">KDays Forum</a>
    </li>
    
    <li>
        <a href="http://www.gal123.com/" target="_blank">绅士导航♂</a>
    </li>
    
    <li>
        <a href="http://www.moe123.com/" target="_blank">萌导航</a>
    </li>
    
</div>
                
                
                <div class="links tab-pane nav bs-sidenav fade" id="sidebar-links">
    
    <li>
        <a href="https://github.com/ZEROKISEKI/" target="_blank">Github</a>
    </li>
    
    <li>
        <a href="https://coding.net/u/SORA1" target="_blank">Coding</a>
    </li>
    
    <li>
        <a href="https://www.zhihu.com/people/aonosora/activities" target="_blank">知乎</a>
    </li>
    
</div>
                
            </div>
        </div>
    </aside>
    
</aside>
            </div>
        </div>
    </div>
    <footer id="gal-footer">
    <div class="container">
        Copyright © 2018 esy Powered by <a href="https://hexo.io/" target="_blank">Hexo</a>.&nbsp;Theme by <a href="https://github.com/ZEROKISEKI" target="_blank">AONOSORA</a>
    </div>
</footer>

<!-- 回到顶端 -->
<div id="gal-gotop">
    <i class="fa fa-angle-up"></i>
</div>
</body>

<script src="/js/activate-power-mode.js"></script>

<script>

    // 配置highslide
	hs.graphicsDir = '/js/highslide/graphics/'
    hs.outlineType = "rounded-white";
    hs.dimmingOpacity = 0.8;
    hs.outlineWhileAnimating = true;
    hs.showCredits = false;
    hs.captionEval = "this.thumb.alt";
    hs.numberPosition = "caption";
    hs.align = "center";
    hs.transitions = ["expand", "crossfade"];
    hs.lang.number = '共%2张图, 当前是第%1张';
    hs.addSlideshow({
      interval: 5000,
      repeat: true,
      useControls: true,
      fixedControls: "fit",
      overlayOptions: {
        opacity: 0.75,
        position: "bottom center",
        hideOnMouseOut: true
      }
    })

    // 初始化aos
    AOS.init({
      duration: 1000,
      delay: 0,
      easing: 'ease-out-back'
    });

</script>
<script>
	POWERMODE.colorful = 'true';    // make power mode colorful
	POWERMODE.shake = 'true';       // turn off shake
	// TODO 这里根据具体情况修改
	document.body.addEventListener('input', POWERMODE);
</script>
<script>
    window.slideConfig = {
      prefix: '/imgs/slide/background',
      ext: 'jpg',
      maxCount: '6'
    }
</script>

<script src="/js/hs.js"></script>
<script src="/js/blog.js"></script>



<script src="/js/oni.js"></script>




</html>